Release Notes

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ScopeMaster  – Release History

Release 1.32  7 December 2018

  • New: Suggested test cases tab for each user story, positive and negative functional test cases
  • New: Suggested test cases report, positive and negative functional test cases
  • Improved: stability and performance

Release 1.31  11 November 2018

  • New: ScopeMaster Plugin for Jira Cloud   Visit the plugin on the Atlasssian Marketplace
  • Improved: additional story improvement suggestions.
  • New: Introducing the ScopeMaster Quality Score
  • Improved: Portfolio level view of quality

Release 1.21  11 October 2018

  • Fixed: Adding a multi-line story via the simple add box.
  • Fixed: Removed “so that” warning, potentially misleading.
  • Fixed: Default user timezone now set.
  • Performance improvements

Release 1.2  August 2018

  • Improved navigation for faster and easier story correction
  • Quickly revert to any previous version of a user story
  • Minor UI improvements and browser compatibility fixes
  • Performance and security improvements
  • Deprecated the text analysis table
  • Improved accuracy of defect reporting, by removing list as an expected function.

Release 1.11 (current),   18 July 2018

  • NEW  Explorer. This is an additional capability that enables portfolio analysis of users and objects.
    • As ScopeMaster analyses requirements it builds up an inventory of the users and objects that are maintained across the software systems of your enterprise.  These are now visible with an easy-to-navigate explorer, so you can see an enterprise-wide view of the applications in which a particular user or object is maintained.
    • Ideal for:
      • Planning and estimating the impact of legislative change on corporate systems.
      • Insight into potential technical debt across systems.
      • Insight relating to application lifecycle planning.
      • Identifying risk areas associated with data security
  • Minor UI improvements (easier navigation for grooming user stories)
  • Improvements to search results.

Release 1.1,   28 June 2018

  • Context specific guidance on improving each story. (learn to improve your stories faster)
  • Improved meta information about a set of requirements, including changes over time and size statistics.

Production release 1.01,   1 June 2018

  • New simple structured user story input (makes it easier to get it right first time!)
  • Jira Integration (import stories directly from your Jira repository)

Production release 1.0,   13 May 2018

  • Minor bug fixes
  • Improved reports and navigation

Pre-Production release 0.91   4 May 2018

  • Easier to find and fix consistency errors – improved users and objects display
  • Improved help pages
  • Improved access to previous versions
  • Corrected interpretation of the word “status”

Pre-Production release 0.89   29 March 2018

  • New ambiguities reporting (thanks to Richard Bender)
  • minor bug fixes

Pre-Production release 0.88,   25 March 2018

  • Improved full screen display and responsive menus
  • Improved defects report
  • Easy navigation back to recently visited user stories

Pre-Production release 0.87,   17 March 2018

  • NEW  Sortable, searchable table, ideal for story grooming
  • Improved text analysis accuracy
  • Simplified defects report
  • Improved performance for very large projects

Pre-Production release 0.85,   6 March 2018

  • NEW  Users can share work with others in their organization.
  • NEW  Share work at the application level: owners can assign read or edit access to others in their organisation.
  • NEW  Requirement text within square brackets will be ignored from sizing analysis.
  • NEW  Requirement export/download as csv.
  • Improved IFPUG Function Point estimates, with function-by-function details report.
  • Improved text analysis accuracy.
  • Improved UI and bug fixes.
  • Improved searching
  • Improved application performance.
  • Improvements to application data security.
  • Major improvements to  server(s) security.

Beta, 14 December 2017

  • Automatically
    • analyses the text to describe software requirements
    • Interprets user story terminology and common active phrases
    • identifies candidate users and objects from the entire body of requirements text
    • detects potentially defective requirements – ambiguous
    • detects potentially defective requirements – omissions
    • detects potentially defective requirements – duplicates
    • Identifies functional data movements
    • Identifies data to be maintained
    • estimates the functional size of the software – in Cosmic Function Points
    • provides estimates for: cost to develop, defect potentials, resource requirements and likely schedules.
  • Accuracy of functional sizing is currently around 70-80% (Nb. manual accuracy can vary by up to 10%)
  • Import by text list or csv
  • Ability to take a snapshot track the size progression.
  • Portfolio view
Prototype, May 2017
  • Import 2 column spreadsheet
  • Basic text analysis engine
  • Initial CFP structure


Taming software requirements and bringing certainty to software development.

Interpreting software requirements